Applying enhanced active learning to predict formation energy

Computational Materials Science, Journal Year: 2024, Volume and Issue: 235, P. 112825 - 112825

Published: Feb. 1, 2024

Language: Английский

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Thermodynamically informed graph for interpretable and extensible machine learning: Martensite start temperature prediction

Calphad, Journal Year: 2024, Volume and Issue: 85, P. 102710 - 102710

Published: May 30, 2024

Language: Английский

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Combination of Empirical Mode Decomposition and Least Squares Support Vector Machine for Gas Utilization Ratio Prediction of Blast Furnace

JOM, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Language: Английский

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High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys

Materials Genome Engineering Advances, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Abstract The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data‐driven material based on machine learning (ML) has emerged as critical tool accelerating materials design. However, absence of robust datasets impedes exploitation in novel RMPEAs. High‐throughput (HTP) calculations have enabled creation such datasets. This study addresses these challenges by developing framework predicting elastic properties RMPEAs, integrating HTP with ML. A big dataset RMPEAs including 4536 compositions was constructed using new proposed method. stacking ensemble regression algorithm combining multilayer perceptron (MLP) gradient boosting decision tree (GBDT) developed, which achieved 92.9% accuracy Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed ML model's robustness. integration provides cost‐effective, efficient, precise alloy strategy, advancing development.

Language: Английский

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A Hybrid Prediction Model for Gas Utilization Rate Based on Blast Furnace Operating Conditions

Metallurgical and Materials Transactions B, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Language: Английский

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Classification enhanced machine learning model for energetic stability of binary compounds

Computational Materials Science, Journal Year: 2024, Volume and Issue: 244, P. 113277 - 113277

Published: Aug. 7, 2024

Language: Английский

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Machine learning prediction of materials properties from chemical composition: Status and prospects

Chemical Physics Reviews, Journal Year: 2024, Volume and Issue: 5(4)

Published: Dec. 1, 2024

In materials science, machine learning (ML) has become an essential and indispensable tool. ML emerged as a powerful tool in particularly for predicting material properties based on chemical composition. This review provides comprehensive overview of the current status future prospects using this domain, with special focus physics-guided (PGML). By integrating physical principles into models, PGML ensures that predictions are not only accurate but also interpretable, addressing critical need sciences. We discuss foundational concepts statistical PGML, outline general framework informatics, explore key aspects such data analysis, feature reduction, composition representation. Additionally, we survey latest advancements prediction geometric structures, electronic properties, other characteristics from formulas. The resource tables listing databases, tools, predictors, offering valuable reference researchers. As field rapidly expands, aims to guide efforts harnessing discovery development.

Language: Английский

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Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase

ACS Omega, Journal Year: 2023, Volume and Issue: 8(40), P. 37317 - 37328

Published: Sept. 27, 2023

The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. formation energy crucial thermochemical datum, the accurate calculation contributes to material design Traditional first-principles calculations demand significant computational time resources. In this study, an innovative machine learning (ML)-based approach accurately predict proposed. This involves utilization six algorithms two model evaluation methods construct ML models. Leveraging comprehensive data set containing 1036 binary configurations phase, trained using 10-fold cross-validation technique, multilayer perceptron (MLP) algorithm achieves mean absolute error (MAE) 23.906 meV/atom. To validate its generalization performance, further validated on 900 ternary configurations, resulting MAE 32.754 Compared with solely traditional calculations, our significantly reduces by at least 52%. Moreover, exhibits exceptional accuracy predicting lattice parameters phase. values for

Language: Английский

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Thermodynamic Modeling of the Bi-Se and Bi-Te Binary Systems

Journal of Phase Equilibria and Diffusion, Journal Year: 2024, Volume and Issue: 45(2), P. 89 - 113

Published: Feb. 23, 2024

Language: Английский

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Critical Assessment of Phase Equilibria in the Al-Co-Ta and Al-Ni-Ta Systems

Journal of Phase Equilibria and Diffusion, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Language: Английский

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